pathway: Advanced Quantitative Methods

Cognitive impairment is a key disabling symptom of psychotic disorders, predictive of worse functional and social outcomes. However, it is often overlooked compared to the positive features such as hallucinations. During my PhD I will use longitudinal data analysis methods to characterise developmental pathways to cognitive impairments in psychosis. I will focus on epigenetic markers, which capture how biological and environmental exposures — such as inflammation, stress, and ageing — influence gene expression, and investigate whether they are associated with cognitive outcomes in people with psychosis. I will utilise diverse population-based cohort studies from across the lifespan.

Previous research suggest that long COVID is under-recorded in Electronic Health Records. I will work with Electronic Health Record data using the OpenSAFELY Trusted Research Environment to investigate how social, economic and demographic factors affect clinical coding of long COVID. By investigating how these factors may affect potential disparity in clinical coding of long COVID, I aim to identify groups of people in which long COVID may be disproportionately under-recorded. In doing this, I hope to improve the use of long COVID codes in these groups which would allow better access to support services for those living with this condition.

In psychology, we tend to be interested in individual minds rather than distributions. Most researchers theorise at the person level yet analyse data at the group level, creating the group-to-person generalisability problem, where group-level effects often fail to represent individual behaviour. My research assesses the extent of this problem by investigating whether group-level effects published in top journals generalise to a population majority, replicating key studies. In the second part, I develop practical tools to help researchers of all statistical abilities recognise and address this problem. Ultimately, my work aims to promote more accurate and transparent research practices across the social sciences.

I will conduct four studies applying advanced quantitative methods and cross-sectional and longitudinal study designs using a current ESRC-funded UK birth cohort from an interdisciplinary perspective, aiming to address the major societal challenge of co-occurring antisocial behaviour (AB) and substance use (SU) in young people, answering unsolved questions about the mechanisms driving the co-occurrence of these conditions from mid-adolescence to early adulthood. Findings will help inform the prevention and intervention programmes for AB or SU alone or in combination, with potential benefits for young people and their families, mental health researchers, education and legal practitioners and services, and policymakers.

My research investigates the associations between family financial hardship and offspring’s mental health and criminal behaviours across childhood, adolescence, and adulthood using the ALSPAC longitudinal datasets. The research aims to identify mediating factors of the associations and explore how shifting financial circumstances impacts mental health through counterfactual analysis. I use advanced quantitative methods, including Growth Curve Modelling and Structural Equation Modelling, to examine developmental trajectories and relationships over time. The findings will inform interventions and policies to address youth mental health, criminal behaviour, and social inequality.

This research investigates factors influencing the academic success of refugee children. Using a mixed-methods approach, the study begins with a literature review exploring the impact of school climate on refugee student wellbeing. Quantitative analysis, employing PISA and UNRWA data, compares educational achievement factors among Palestinian refugees in UNRWA and non-UNRWA settings, focusing on family support and school belonging. Qualitative interviews with UNRWA educators provide additional insights into student achievement and wellbeing. The combined findings aim to enhance the educational experiences of refugee children

I am exploring new ways to trace the development of the transport network to then investigate how changes in accessibility are linked to socioeconomic developments.

I plan to use Cognitive Diagnostic Modelling with large-scale data sets such as TIMSS or KS2 SATs to better understand mathematics learning progress in England.

Most academic tests result in a single grade (e.g. a B in A-level history), but Cognitive Diagnostic Modelling aims to produce a profile detailing each of the subject skills that a test taker has mastered. This information can be used to find groups with similar mastery profiles and explore possible causes for these patterns of learning. By investigating mathematics learning, I hope to identify policy and practice routes to better maths learning outcomes.

Approximately 5-10% of children in care in the UK experience three or more placements in a single year. My research focuses on investigating the characteristics and experiences of children who experience significant placement instability, with the overall aim of enhancing our understanding of the relationship between placement instability and mental health outcomes for children in care. Utilising a mixed-methods approach, I will capitalise on the quantitative analysis of large administrative datasets and compliment this with richer qualitative data in an attempt to identify individual and service-level factors that may alleviate or intensify distress associated with placement instability

My research aims to uncover the complex mechanisms behind the cognitive and attitudinal aptitudes of schooled teenagers to understand and tackle global issues. This will be through three interlinked studies analysing new Programme for International Student Assessment (PISA) 2018 data on Global Competence (GC) assessments with a combination of Multilevel Modelling and Structural Equation Modelling. Each study will address a specific issue: (1) the relationships between GC with student and school characteristics, (2) the role of student wellbeing and school climate measures in determining GC, (3) the impact of including global issues in national policies and school curriculums on GC.